The impact of introducing deep learning based [18F]FDG PET denoising on EORTC and PERCIST therapeutic response assessments in digital PET/CT-Reference-Cited by-同舟云学术

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The impact of introducing deep learning based [18F]FDG PET denoising on EORTC and PERCIST therapeutic response assessments in digital PET/CT

Published:2024-08-10 Issue:1 Volume:14 Page:
ISSN:2191-219X
Container-title:EJNMMI Research
language:en
Short-container-title:EJNMMI Res

Author:

Weyts Kathleen^ORCID,Lequesne Justine,Johnson Alison,Curcio Hubert,Parzy Aurélie,Coquan Elodie,Lasnon Charline

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s13550-024-01128-z.pdf

Reference38 articles.

1. Global Burden of Disease (GBD). [Internet]. [cited 2023 Aug 3]. https://www.healthdata.org/research-analysis/gbd.

2. Cancer Progress Timeline [Internet]. ASCO. [cited 2023 Aug 3]. https://old-prod.asco.org/news-initiatives/cancer-progress-timeline.

3. Ben-Haim S, Ell P. 18F-FDG PET and PET/CT in the evaluation of Cancer Treatment Response. J Nucl Med. 2009;50:88–99. https://doi.org/10.2967/jnumed.108.054205.

4. Gallamini A, Zwarthoed C, Borra A. Positron Emission Tomography (PET) in Oncology. Cancers. 2014;6:1821–89. https://doi.org/10.3390/cancers6041821.

5. Hirata K, Tamaki N. Quantitative FDG PET Assessment for Oncology Therapy. Cancers. 2021;13:869. https://doi.org/10.3390/cancers13040869.

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